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@piyushgarg-dev
piyushgarg-dev / README.md
Last active April 29, 2026 11:14
Kafka Crash Course

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
@rohitg00
rohitg00 / llm-wiki.md
Last active April 29, 2026 11:10 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

  • Shall i implement it?
  • No ...
@OTDE
OTDE / html-dsl-code-golf.jl
Created April 29, 2026 00:55
Tiny HTML Julia DSL
const Attribute = Pair{Symbol,<:Any}
const BareElement = Tuple{Symbol,Vararg{Any}}
const HTMLElement = Tuple{Symbol,Union{NamedTuple,Nothing},Vararg{Any}}
const VoidElement = Tuple{Symbol,Union{NamedTuple,Nothing}}
const VoidTags = (:area, :base, :br, :col, :embed, :hr, :img, :input, :link, :meta, :param, :source, :track, :wbr)
escapehtml(text) = replace(text, '&' => "&amp;", '<' => "&lt;", ">" => "&gt;", '"' => "&quot;", ''' => "&#39;")
kebabcase(symbol::Symbol) = replace(string(symbol), '_' => '-')
struct Raw{Character} end
@burkeholland
burkeholland / 4.1.chatmode.md
Last active April 29, 2026 11:04
4.1 Beast Mode v2
description 4.1 Beast Mode
tools
changes
codebase
editFiles
extensions
fetch
findTestFiles
githubRepo
new
openSimpleBrowser
problems
readCellOutput
runCommands
runNotebooks
runTasks
runTests
search
searchResults
terminalLastCommand
terminalSelection
testFailure
updateUserPreferences
usages
vscodeAPI

You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user.